An Integrated Framework for Spatio-Temporal-Textual Search and Mining

Bingsheng Wang
Haili Dong
Arnold Boedihardjo
Chang-Tien Lu
Harland Yu
Ing-Ray Chen
20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2012), ACM, 2 Penn Plaza, Suite 701, New York, NY 10121, pp. 570-573
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This paper presents an integrated framework for Spatio-Temporal-Textual (STT) information retrieval and knowledge discovery system. The proposed ensemble framework contains an efficient STT search engine with multiple indexing, ranking and scoring schemes, an effective STT pattern miner with Spatio-Temporal (ST) analytics, and novel STT topic modeling. Specifically, we design an effective prediction prototype with a third-order linear regression model, and present an innovative STT topic modeling relevance ranker to score documents based on inherent STT features under topical space. We demonstrate the framework with a crime dataset from the Washington, DC area from 2006 to 2010 and a global terrorism dataset from 2004 to 2010.